Knowledge graphs (KGs) are informative knowledge bases for various applications such as semantic web search or question answering. Since KGs often have missing facts mainly due to large construction costs, KG completion is a fundamental problem. Nowadays, as KGs have become large-scale, KG completion has become gradually intractable for classical computers. One promising direction is to utilize quantum circuits to capture complex structural information that entities and relations in KGs have. By encoding entities into quantum states and representing relations between entities as quantum circuits running on the quantum states, it becomes able to score and predict plausible missing entities or relations. However, it is still unclear how to design the quantum circuits to enhance KG completion accuracy. In this paper, we evaluate variations of quantum circuits in terms of KG completion accuracy, and investigate the relationship between the accuracy and the quality of the circuits (e.g., expressibility and entangling capacity). The experiments using a real KG show some correlation between the completion accuracy and the goodness of the circuits.
Knowledge graphs (KGs) are informative knowledge bases for various applications such as semantic web search or question answering. Since KGs often have missing facts mainly due to large construction costs, KG completion is a fundamental problem. Nowadays, as KGs have become large-scale, KG completion has become gradually intractable for classical computers. One promising direction is to utilize quantum circuits to capture complex structural information that entities and relations in KGs have. By encoding entities into quantum states and representing relations between entities as quantum circuits running on the quantum states, it becomes able to score and predict plausible missing entities or relations. However, it is still unclear how to design the quantum circuits to enhance KG completion accuracy. In this paper, we evaluate variations of quantum circuits in terms of KG completion accuracy, and investigate the relationship between the accuracy and the quality of the circuits (e.g., expressibility and entangling capacity). The experiments using a real KG show some correlation between the completion accuracy and the goodness of the circuits.
Embedding of a knowledge graph(KG) entities and relations in the form of vectors is an important aspect for the manipulation of the KG database for several downstream tasks, such as link prediction, knowledge graph completion, and recommendation. Because of the growing size of the knowledge graph databases, it has become a daunting task for the classical computer to train a model efficiently. Quantum computer can help speedup the embedding process of the KGs by encoding the entities into a variational quantum circuit of polynomial depth. Usually, the time complexity for such variational circuit-dependent quantum classical algorithms for each epoch is O(Npoly(logM)), whereNis number of elements in the knowledge graph andMis the number of features of each entities of the knowledge graph. In this article we exploit additional quantum advantage by training multiple elements of KG in superpositions, thereby reducing the computing time further for the knowledge graph embedding model.
Variational Quantum Algorithms (VQAs) combine quantum circuits with classical optimization to tackle problems that may benefit from the capabilities of near-term quantum hardware. In knowledge graph embedding, recent proposals based on this approach follow a similar overall architecture but differ in the way they compute the score function and in the number of qubits they require. One design uses n+1 qubits and obtains the score through a switch test on an ancillary qubit, while another employs 2n+1 qubits and applies a swap test between two registers. In both cases, entities and relations are represented in a Hilbert space of dimension d=2n, with comparable computational cost and the same mean squared error loss. This work introduces a unified framework that captures the two schemes and makes it possible to explore new variants. Within this setting, we propose an alternative that keeps the intuitive meaning of the score function while dispensing with ancillary qubits and entangled measurements. The result is a model better suited to current NISQ devices, reducing hardware demands without sacrificing interpretability.
Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been a major area of research recently in classical machine learning. Because the size of knowledge graphs are becoming larger, one of the natural choices is to exploit quantum computing for knowledge graph embedding. Recently, a hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained. One of the important aspects in knowledge graph embedding is the sampling of negative triples, which plays a crucial role in efficient training of the model. In classical machine learning various negative sampling strategies have been studied. In quantum knowledge graph embedding model, although we can use these strategies in principle, it is natural to ask if we can exploit quantum advantage in negative sampling. In this article we study such a negative sampling strategy, which exploits quantum superposition, and evaluate the model's performance with a knowledge graph database.
In this work, we propose the first quantum Ansätze for the statistical relational learning on knowledge graphs using parametric quantum circuits. We introduce two types of variational quantum circuits for knowledge graph embedding. Inspired by the classical representation learning, we first consider latent features for entities as coefficients of quantum states, while predicates are characterized by parametric gates acting on the quantum states. For the first model, the quantum advantages disappear when it comes to the optimization of this model. Therefore, we introduce a second quantum circuit model where embeddings of entities are generated from parameterized quantum gates acting on the pure quantum state. The benefit of the second method is that the quantum embeddings can be trained efficiently meanwhile preserving the quantum advantages. We show the proposed methods can achieve comparable results to the state-of-the-art classical models, e.g., RESCAL, DistMult. Furthermore, after optimizing the models, the complexity of inductive inference on the knowledge graphs might be reduced with respect to the number of entities.
Abstract In this work, the first quantum Ansätze for the statistical relational learning on knowledge graphs using parametric quantum circuits are proposed. Two types of variational quantum circuits for knowledge graph embedding are introduced. Inspired by the classical representation learning, latent features for entities are first considered as coefficients of quantum states, while predicates are characterized by parametric gates acting on the quantum states. For the first model, the quantum advantages disappear when it comes to the optimization of this model. Therefore, a second quantum circuit model is introduced where embeddings of entities are generated from parameterized quantum gates acting on the pure quantum state. The benefit of the second method is that the quantum embeddings can be trained efficiently meanwhile preserving the quantum advantages. It is shown that the proposed methods can achieve comparable results to the state‐of‐the‐art classical models, for example, RESCAL , DistMult . Furthermore, after optimizing the models, the complexity of inductive inference on the knowledge graphs might be reduced with respect to the number of entities.
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variational quantum circuits (VQCs) using graph-based Bayesian optimization with a graph neural network (GNN) surrogate. Circuits are represented as graphs and mutated and selected via an expected improvement acquisition function informed by surrogate uncertainty with Monte Carlo dropout. Candidate circuits are evaluated with a hybrid quantum-classical variational classifier on the next generation firewall telemetry and network internet of things (NF-ToN-IoT-V2) cybersecurity dataset, after feature selection and scaling for quantum embedding. We benchmark our pipeline against an MLP-based surrogate, random search, and greedy GNN selection. The GNN-guided optimizer consistently finds circuits with lower complexity and competitive or superior classification accuracy compared to all baselines. Robustness is assessed via a noise study across standard quantum noise channels, including amplitude damping, phase damping, thermal relaxation, depolarizing, and readout bit flip noise. The implementation is fully reproducible, with time benchmarking and export of best found circuits, providing a scalable and interpretable route to automated quantum circuit discovery.